dust storm – HPCwirehttps://www.hpcwire.com
Since 1987 - Covering the Fastest Computers in the World and the People Who Run ThemThu, 22 Feb 2018 00:27:19 +0000en-UShourly1https://wordpress.org/?v=4.9.460365857Dust Storms Put GPU CPU Performance to the Testhttps://www.hpcwire.com/2013/06/06/dust_storms_put_gpu_cpu_performance_to_the_test/?utm_source=rss&utm_medium=rss&utm_campaign=dust_storms_put_gpu_cpu_performance_to_the_test
https://www.hpcwire.com/2013/06/06/dust_storms_put_gpu_cpu_performance_to_the_test/#respondThu, 06 Jun 2013 07:00:00 +0000http://www.hpcwire.com/?p=4007A team of researchers modeling environmental events looked to the relative efficiencies of using GPU and CPU approaches for their processing and rendering. While CPUs were needed due to limited on-board GPU memory....

]]>Visualizing 3D and 4D environmental data is necessary for greater understanding and prediction of environmental events.

Researchers from the Center for Intelligent Spatial Computing and the University of Denver are trying to better grasp how they can harness both CPUs and GPUs together to speed a sample geovisualization process using dust storms as the subject..

By visualizing these storms, the team was able to develop a 3D/4D framework for geovisualization that includes everything from preprocessing, reprojection, interpolation and rendering.

While the CPU was an important component of their initial project, especially in terms of preprocessing the data that couldn’t be held in the GPU’s on-board memory, GPUs presented a higher performance and more efficient solution than CPUs.

They were then able to compare the performance differences between GPUs and CPUs. Their findings revealed that multicore CPUs and manycore GPUs can improve the efficiency of calculations and rendering using multithreading techniques. They also found that given the same amount of data, when increasing the size of blocks of GPUs for a coordinate transformation, the executing time of the interpolation and rendering is consistently reduced after hitting a peak.

The team also concluded that the best performance results obtained by GPU implementations in all the three major processes are usually faster than CPU-based implementations, although the best performance with the rendering component is similar between GPUs and CPUs.

On the memory front, they note that the on-board memory of the GPU limits the capabilities of processing large volume data, thus they needed to do preprocessing on the CPU. Still the efficiency of their project was hit by the relatively high latency of the data flow between GPU and CPU.